Identification of cuproptosis-related molecular subtypes as a biomarker for differentiating active from latent tuberculosis in children.

BACKGROUND

Cell death plays a crucial role in the progression of active tuberculosis (ATB) from latent infection (LTBI). Cuproptosis, a novel programmed cell death, has been reported to be associated with the pathology of various diseases. We aimed to identify cuproptosis-related molecular subtypes as biomarkers for distinguishing ATB from LTBI in pediatric patients.

METHOD

The expression profiles of cuproptosis regulators and immune characteristics in pediatric patients with ATB and LTBI were analyzed based on GSE39939 downloaded from the Gene Expression Omnibus. From the 52 ATB samples, we investigated the molecular subtypes based on differentially expressed cuproptosis-related genes (DE-CRGs) via consensus clustering and related immune cell infiltration. Subtype-specific differentially expressed genes (DEGs) were found using the weighted gene co-expression network analysis. The optimum machine model was then determined by comparing the performance of the eXtreme Gradient Boost (XGB), the random forest model (RF), the general linear model (GLM), and the support vector machine model (SVM). Nomogram and test datasets (GSE39940) were used to verify the prediction accuracy.

RESULTS

Nine DE-CRGs (NFE2L2, NLRP3, FDX1, LIPT1, PDHB, MTF1, GLS, DBT, and DLST) associated with active immune responses were ascertained between ATB and LTBI patients. Two cuproptosis-related molecular subtypes were defined in ATB pediatrics. Single sample gene set enrichment analysis suggested that compared with Subtype 2, Subtype 1 was characterized by decreased lymphocytes and increased inflammatory activation. Gene set variation analysis showed that cluster-specific DEGs in Subtype 1 were closely associated with immune and inflammation responses and energy and amino acids metabolism. The SVM model exhibited the best discriminative performance with a higher area under the curve (AUC = 0.983) and relatively lower root mean square and residual error. A final 5-gene-based (MAN1C1, DKFZP434N035, SIRT4, BPGM, and APBA2) SVM model was created, demonstrating satisfactory performance in the test datasets (AUC = 0.905). The decision curve analysis and nomogram calibration curve also revealed the accuracy of differentiating ATB from LTBI in children.

CONCLUSION

Our study suggested that cuproptosis might be associated with the immunopathology of Mycobacterium tuberculosis infection in children. Additionally, we built a satisfactory prediction model to assess the cuproptosis subtype risk in ATB, which can be used as a reliable biomarker for the distinguishment between pediatric ATB and LTBI.